基于特征融合的KLPCCA及其在人脸识别中的应用

对发掘人脸图像中的高维非线性结构，将加核及典型相关分析两种思想同时引入局部保留投影算法中，提出了一种新的基于核的局部保持典型相关分析（Kemel base Locality Preserving Canonical Correction Analvsis，KLPCCA）非线性子空间人脸识别算法并给出了其推导过程。算法首先利用核的方法提取人脸图像中的非线性信息，然后通过局部保持投影算法做一线性映射，从而更简单准确的进行人脸识别。在ORL上的试验证明了该文所提算法的有效性。

Facial Recognition Based on Hybrid Features Fused By KLPCCA

In this paper,considering kernel and canonical correction analysis,a new method named kernel base Locality preserving canonical correction analysis ahhoithm,which aims at discovering an embedding that preserves knonlinear information is proposed for face representation and recognition. In this algorithm ,first,the nonlinear kernel mapping is used to map the face data into an implicit feature space, and then a linear transformation which produces a function is perfoumed to preserve locatlity geometric structures of the face image.epreiments based on ORL face database demonstrate the effectiveness of the new algorithm.